The Structure of Human Knowledge

Games and knowledge (The Structure of Human Knowledge as Game II)

Why are games consisting of knowledge tests so popular? In 2004 it was calculated that Trivial Pursuit had sold around 88 million copies worldwide, and game shows like Jeopardy and the 64000 dollar question have become international hits. At their core, these games are surprisingly simple. They are about what you know, about if you can answer questions (or find questions for answers in the case of Jeopardy). So why are they so engaging? Why are they so popular? Why do we find knowing something so satisfying?

When we study human knowledge as a game, it is worthwhile also to explore why we enjoy playing games that build on knowledge so much. There is a subtle dominance built into these games – the one who knows more wins – and to win is oddly satisfying, even though there likely is a significant element of randomness in what questions come up. (It is easy to construct paths through the questions in TP that you can answer effortlessly, and equally easy to construct the opposite – an impossible path for you to get through – the design conundrum here becomes one of what the ideal difficulty is. One way to think about this would be to think about how long the average path to win should be for someone playing the game on their own).

So, maybe it is that simple: we enjoy the feeling of superiority that comes with knowing more than others. We revere the expert who has spent a lot of time learning a subject through and through, and respect someone who can traverse the noise of our information space with ease.

Should we expect that to change – and if so why? Sales of Trivial Pursuit seems to have tapered off. Jeopardy no longer holds our interest. Would anyone sit down and watch the 64000 dollar question today? Or is the advance of new technology and new knowledge paradigms killing these games? The rise of reality TV and game shows that emphasize the physical effort can in a sense be seen as a decline of the knowledge games that we once preferred to simple physical humor or emotional drama. Maybe the hypothesis now needs to be this:

(i) The sinking cost of acquiring knowledge has made knowledge less valuable, and hence less entertaining and exciting. Less playable.

At the same time we see the rise of other board games, and a curious increase in the number of people who play them. The board games that are popular now require the mastering of a method of play, a bit like mastering an instrument, and the aficionados can play hundreds of games, having master the game mechanics of a wide range of different games. There is a reversal here: from a world in which we played human knowledge by testing what we knew, to one where we are adding different new gaming mechanics to human knowledge and allow these models of challenges, problems and the world to be absorbed by our body of knowledge as new material.
Rather than play on our out of human knowledge we play into it, in a sense.

It makes sense that games like these – where the skill is mastering the game mechanics and not excelling at knowing things – should become more popular as the cost of acquiring knowledge goes down. Should we welcome this or fight it? One could argue that the problem here is that the utility of knowing many things – almost Bildung – is much higher than the utility of mastering different gaming mechanics. But that would be to simple, and perhaps also a little silly. Maybe the way to think about this is to say that the nature of what is valuable _human_ knowledge is changing. What is it that we need to know as humans in a world where knowledge is distributed across human minds and silicon systems? What is the optimal such distribution?

Where fact acquisition cost is low, and complexity of problems is high – the real value for us as humans lie in knowledge and construction of models. The many model thinker today has an advantage over those that have mastered no or few models. Understanding and mastering the gaming mechanics of a board game rather than remembering a lot of facts about sports becomes much more interesting and valuable – and resonates much more with the kind of computational thinking we want to instil in our children.

As we bring this back to the study of the structure of human knowledge as game, we realize that one important thing here is to explicate and understand the different mechanics we use to travel through our knowledge, and that brings us back to the thought experiment we started with, the idea of the glass bead game. There are multiple different mechanics available to us as we start to link together the different fields and themes of human knowledge, and maybe we need to also allow for these to carry meaning – the way we connect different fields could also be different depending on the fields and the themes?

There are a lot of other questions here, and things to come back to and research. A few questions that I want to look at more closely as we progress are the following:

a) How many kinds of board games are there? What classes of game mechanics do we recognize in research?
b) How do we categorize human knowledge in knowledge games like Trivial Pursuit? Why? Are there categorizations of human knowledge that are more playable than others?
c) What is the ideal difficulty of a knowledge game? Of a “mechanics” game? Where do we put the difficulty? What are good models for understanding game complexity?

Our interpretation of knowledge as a way to play is another aspect that we will return to as we get closer to Gadamer.

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